V2G-participated group microgrid energy transaction and collaborative scheduling joint optimization method

Abstract

The invention provides a V2G-participated group microgrid energy transaction and collaborative scheduling joint optimization method, and relates to the technical field of power market and power system optimization operation, and the method comprises the steps: obtaining system operation parameters, the system comprises a distributed power supply, an energy storage system, an electric vehicle group and a charging station facility in a group microgrid, according to the obtained operation parameters, constructing an objective function of group micro-grid energy transaction and V2G collaborative scheduling joint optimization; according to the obtained operation parameters, constraint conditions are constructed, and the constraint conditions comprise basic constraints of system operation and charge and discharge and mobile energy storage related constraints under V2G participation; constructing an energy transaction settlement model among the group micro-grids according to the acquired operation parameters; and performing joint optimization based on the constructed objective function, constraint conditions and an energy transaction settlement model. By the adoption of the scheme, the mobile energy storage characteristic of the electric vehicle is fully utilized, and the flexible adjusting capacity of the system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power market and power system optimization operation, and in particular to a method, system and device for joint optimization of energy trading and coordinated dispatch of group microgrids involving V2G. Background Art

[0002] As the proportion of renewable energy in the power system increases, the volatility, randomness, and intermittency of its power generation will place greater pressure on the power system's operation. At the same time, the large-scale integration of electric vehicles also brings new challenges to the distribution network, requiring a certain level of flexible adjustment and response capabilities to cope with potential power fluctuations and voltage over-limits caused by renewable energy and electric vehicle charging and discharging, placing higher demands on system flexibility.

[0003] From the perspective of system operational safety, both renewable energy output and load demand within a microgrid cluster exhibit random fluctuations. Lack of sufficient system regulation will lead to localized power imbalances. Currently, the system primarily relies on fixed energy storage devices to address power balancing. However, with the increase in installed renewable energy capacity and the penetration of electric vehicles, the burden of system regulation will increase. Insufficient regulation capacity will necessitate limiting renewable energy output or implementing load-side management measures, impacting system power reliability.

[0004] From the perspective of system economics, insufficient regulation capacity will create regional power supply and demand imbalances, leading to higher local electricity prices and increased energy transaction costs. Furthermore, the lack of an effective energy trading mechanism among microgrids makes it difficult to achieve optimal energy allocation within the region, hindering market efficiency. Therefore, to improve system economics, it is necessary to establish a comprehensive energy trading mechanism among microgrids and fully leverage the value of V2G services for electric vehicles.

[0005] Electric vehicles are widely distributed and responsive. Furthermore, V2G technology provides bidirectional energy flow and rapid response to system power fluctuations, making them an important option for improving distribution network flexibility both now and in the future. Therefore, it is necessary to conduct research on energy trading and coordinated scheduling in microgrids based on V2G participation, fully leveraging the mobile energy storage characteristics of electric vehicles and improving the system's flexible regulation capabilities. Summary of the Invention

[0006] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the first purpose of this application is to propose a joint optimization method for energy trading and coordinated scheduling of group microgrids involving V2G, which fully utilizes the mobile energy storage characteristics of electric vehicles and improves the flexible adjustment capability of the system, which is of great significance for improving the operating efficiency of the distribution network, promoting the consumption of new energy and realizing the optimal configuration of energy.

[0008] The second purpose of this application is to propose a joint optimization system for energy trading and coordinated scheduling of group microgrids with V2G participation.

[0009] The third object of this application is to provide a computer device.

[0010] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0011] To achieve the above objectives, the first embodiment of the present application proposes a joint optimization method for energy trading and coordinated scheduling of a group microgrid with V2G participation, including:

[0012] Obtaining system operating parameters, where the system includes distributed power sources, energy storage systems, electric vehicle fleets, and charging station facilities within the group microgrid;

[0013] Based on the obtained operating parameters, the objective function of joint optimization of group microgrid energy trading and V2G collaborative scheduling is constructed;

[0014] Based on the acquired operating parameters, constraints are constructed. These constraints include basic system operation constraints and constraints related to charging and discharging under V2G participation and mobile energy storage.

[0015] Based on the obtained operating parameters, an energy transaction settlement model between microgrids is constructed;

[0016] Joint optimization is performed based on the constructed objective function, constraints and energy trading settlement model.

[0017] To achieve the above objectives, a second embodiment of the present invention proposes a V2G-participated group microgrid energy trading and coordinated scheduling joint optimization system, including:

[0018] a parameter acquisition module for acquiring system operating parameters, wherein the system includes distributed power sources, energy storage systems, electric vehicle groups, and charging station facilities within the group microgrid;

[0019] The target construction module is used to construct the objective function of the joint optimization of group microgrid energy trading and V2G collaborative scheduling based on the obtained operating parameters;

[0020] The constraint construction module is used to construct constraint conditions based on the acquired operating parameters. The constraint conditions include basic constraints on system operation and constraints related to charging and discharging and mobile energy storage under the participation of V2G;

[0021] Energy transaction model construction module, used to build an energy transaction settlement model between group microgrids based on the acquired operating parameters;

[0022] The joint optimization module is used to perform joint optimization based on the constructed objective function, constraints and energy trading settlement model.

[0023] To achieve the above-mentioned purpose, the third embodiment of the present invention proposes a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned V2G-participated group microgrid energy trading and collaborative scheduling joint optimization method is implemented.

[0024] In order to achieve the above-mentioned objectives, the fourth aspect of the present invention proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor, the above-mentioned V2G-participated group microgrid energy trading and collaborative scheduling joint optimization method can be executed.

[0025] The V2G-participated group microgrid energy trading and coordinated scheduling joint optimization method, system and device of the embodiment of the present application aim to minimize system operating costs and optimize reliability, take into account the wide distribution and rapid response of electric vehicles, and consider the spatiotemporal distribution constraints and charging and discharging characteristics of electric vehicle groups, so as to coordinate with the distributed power supply and energy storage system in the group microgrid to jointly cope with the uncertainty brought about by renewable energy access and load fluctuations, improve the system's flexible adjustment capability, and tap the mobile energy storage potential of electric vehicle groups, which is of great significance for improving the operating efficiency of the distribution network, promoting the consumption of new energy and realizing the optimal allocation of energy.

[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flowchart of a joint optimization method for energy trading and coordinated scheduling of a group microgrid with V2G participation provided in Example 1 of the present application;

[0029] Figure 2A structural diagram of a V2G-participated group microgrid energy trading and collaborative scheduling joint optimization system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes, with reference to the accompanying drawings, a method, system, and apparatus for joint optimization of energy trading and coordinated scheduling of a group microgrid involving V2G in an embodiment of the present application.

[0032] Figure 1 This is a flow chart of a joint optimization method for energy trading and coordinated scheduling of a group microgrid with V2G participation provided in Example 1 of the present application.

[0033] like Figure 1 As shown in FIG, the V2G-participated group microgrid energy transaction and coordinated scheduling joint optimization method includes the following steps:

[0034] Step 101, obtaining system operating parameters, wherein the system includes distributed power sources, energy storage systems, electric vehicle groups, and charging station facilities within a group microgrid;

[0035] Step 102: construct an objective function for joint optimization of group microgrid energy trading and V2G collaborative scheduling based on the acquired operating parameters;

[0036] In this embodiment, the objective function constructed is:

[0037] minF=ω1F cost +ω2F rel +ω3F env

[0038]

[0039] Where t represents the dispatch period index, t∈[1,T]; i represents the microgrid index, i∈[1,N]; j represents the electric vehicle index, j∈[1,M]; P MG,i,t represents the output of the i-th microgrid in period t; P h2G,j,t P represents the charging and discharging power of the jth electric vehicle in period t; grid,t P represents the exchange power with the main network during period t; load,i,t represents the total load demand during period t; C MG,i,t represents the microgrid operation cost coefficient; C V2G,j,trepresents the V2G service cost coefficient; C loss,j,t represents the battery loss cost coefficient; C grid,t represents the load demand; represents the expected V2G response power; ε MG,i represents the carbon emission coefficient of the microgrid; ε V2G,j Indicates the carbon emission coefficient of V2G services.

[0040] Step 103: Constructing constraint conditions based on the acquired operating parameters, wherein the constraint conditions include basic constraints on system operation and constraints related to charging and discharging and mobile energy storage under the participation of V2G;

[0041] In this embodiment, the basic constraints on system operation include:

[0042] Power balance constraints:

[0043]

[0044] Microgrid output constraints:

[0045]

[0046] Grid exchange power constraints:

[0047]

[0048] Node voltage constraints:

[0049]

[0050] Line power constraints:

[0051]

[0052] in, Indicates the minimum / maximum output limit of the microgrid; Indicates the minimum / maximum power limit exchanged with the grid; V i,t represents the voltage of node i during period t; Indicates the minimum / maximum limit of the node voltage; P line,l,t represents the transmission power of line l in period t; Indicates the maximum transmission power limit of the line.

[0053] In this embodiment, the constraints related to charging, discharging, and mobile energy storage under V2G participation include:

[0054] V2G charging and discharging power constraints:

[0055]

[0056] Dynamic constraints of electric vehicle SOC:

[0057]

[0058] SOC operating range constraints:

[0059]

[0060] Charge and discharge mutual exclusion constraints:

[0061]

[0062] in, represents the charging power of electric vehicle j during period t; represents the discharge power of electric vehicle j during period t; Respectively represent the minimum / maximum limit of charging power; Respectively represent the minimum / maximum limit of discharge power; SOC j,t represents the state of charge of electric vehicle j during period t; η ch ,η dis Respectively represent the charge / discharge efficiency; represents the battery capacity of electric vehicle j; Indicates the minimum / maximum limit of SOC; They represent the charge / discharge state indicator variables (0-1 variables) respectively; Δt represents the time interval.

[0063] Step 104: construct an energy transaction settlement model among the group microgrids based on the acquired operating parameters;

[0064] In this embodiment, an energy transaction settlement model among a group of microgrids is constructed, including:

[0065] Total cost of energy trading settlement:

[0066]

[0067] Energy transaction costs between microgrids

[0068] Transaction costs between microgrids:

[0069] C tr,i,j,t =P tr,i,j,t ·λ tr,t ·(1+β loss )

[0070] Transaction power balance constraints:

[0071]

[0072] Transaction power limit:

[0073]

[0074] V2G service settlement costs

[0075] V2G service cost:

[0076]

[0077] Battery loss cost:

[0078] C deg,j,t =k deg ·|P V2G,j,t |·Δt

[0079] Transaction price mechanism

[0080] Real-time transaction electricity prices:

[0081]

[0082] V2G service compensation price:

[0083]

[0084] Where: C total represents the total settlement cost; C tr,i,j,t represents the transaction cost between microgrids i and j in period t; C V2G,j,t represents the V2G service cost of electric vehicle j in period t; P tr,i,j,t represents the transaction power between microgrids i and j in period t; tr,t represents the transaction electricity price during period t; β loss Indicates the line loss compensation coefficient; Indicates the maximum allowed transaction power; represents the V2G discharge power; λ V2G,t represents the V2G service compensation price; C dee,j,t represents the battery loss cost; k deg Represents the battery loss coefficient; λ base represents the basic electricity price; k dem represents the demand response coefficient; P dem,t represents the required power during period t; Indicates the maximum required power; k V2G represents the V2G price adjustment coefficient; Indicates the required V2G response power; Indicates the maximum V2G responsiveness.

[0085] Step 105: Perform joint optimization based on the constructed objective function, constraints, and energy transaction settlement model.

[0086] The V2G-participated group microgrid energy trading and coordinated scheduling joint optimization method of the embodiment of the present application aims to minimize system operating costs and optimize reliability, takes into account the wide distribution and rapid response of electric vehicles, and considers the spatiotemporal distribution constraints and charging and discharging characteristics of electric vehicle groups, so that they can coordinate with the distributed power sources and energy storage systems in the group microgrid to jointly cope with the uncertain effects brought about by renewable energy access and load fluctuations, improve the system's flexible adjustment capabilities, and tap the mobile energy storage potential of electric vehicle groups. It is of great significance to improving the operating efficiency of the distribution network, promoting the consumption of new energy, and realizing the optimal allocation of energy.

[0087] In order to implement the above embodiments, the present application also proposes a V2G-participated group microgrid energy trading and collaborative scheduling joint optimization system.

[0088] Figure 2 A structural diagram of a V2G-participated group microgrid energy trading and collaborative scheduling joint optimization system provided in an embodiment of the present application.

[0089] like Figure 2 As shown in FIG, the V2G-participated group microgrid energy trading and coordinated dispatch joint optimization system includes:

[0090] a parameter acquisition module for acquiring system operating parameters, wherein the system includes distributed power sources, energy storage systems, electric vehicle groups, and charging station facilities within the group microgrid;

[0091] The target construction module is used to construct the objective function of the joint optimization of group microgrid energy trading and V2G collaborative scheduling based on the obtained operating parameters;

[0092] The constraint construction module is used to construct constraint conditions based on the acquired operating parameters. The constraint conditions include basic constraints on system operation and constraints related to charging and discharging and mobile energy storage under the participation of V2G;

[0093] Energy transaction model construction module, used to build an energy transaction settlement model between group microgrids based on the acquired operating parameters;

[0094] The joint optimization module is used to perform joint optimization based on the constructed objective function, constraints and energy trading settlement model.

[0095] It should be noted that the above explanation of the embodiment of the joint optimization method for energy trading and coordinated scheduling of group microgrids with V2G participation is also applicable to the joint optimization system for energy trading and coordinated scheduling of group microgrids with V2G participation in this embodiment, and will not be repeated here.

[0096] In order to implement the above embodiments, the present invention further proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the above embodiments is implemented.

[0097] In order to implement the above embodiments, the present invention further proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method of the above embodiments is implemented.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0099] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0100] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0101] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0102] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0104] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0105] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A joint optimization method for energy trading and coordinated scheduling of a group microgrid with V2G participation, characterized in that: include: Obtaining system operating parameters, wherein the system includes distributed power sources, energy storage systems, electric vehicle groups, and charging station facilities within the group microgrid; Based on the obtained operating parameters, the objective function of joint optimization of group microgrid energy trading and V2G collaborative scheduling is constructed; Based on the acquired operating parameters, constraints are constructed, wherein the constraints include basic constraints on system operation and constraints related to charging and discharging and mobile energy storage under the participation of V2G; Based on the obtained operating parameters, an energy transaction settlement model between microgrids is constructed; Joint optimization is performed based on the constructed objective function, constraints and energy trading settlement model.

2. The method according to claim 1, wherein The objective function is: minF=ω1F cost +ω2F rel +ω3F env Among them, ω is the weight coefficient, F cost is the cost objective function, which represents the total cost of system operation and is expressed as: F rel is the reliability objective function, which indicates the reliability of system operation and is expressed as: F env is the environmental objective function, which represents the environmental impact of the system operation and is expressed as: Where t represents the scheduling period index, t∈[1,T], i represents the microgrid index, i∈[1,N], P MG,i,t represents the output of the i-th microgrid in period t, C MG,i,t represents the microgrid operation cost coefficient, j represents the electric vehicle index, j∈[1,M], P V2g,j,t represents the charging and discharging power of the jth electric vehicle in period t, C V2g,j,t represents the V2G service cost coefficient, C loss,j,t represents the battery loss cost coefficient, P grid,t represents the exchange power with the main network during period t, C grid,t represents the load demand, P load,i,t represents the total load demand during period t, represents the expected V2G response power, ε MG,i represents the carbon emission coefficient of the microgrid, ε V2G,j Indicates the carbon emission coefficient of V2G services.

3. The method according to claim 1, wherein The basic constraints of the system operation include power balance constraints, microgrid output constraints, grid exchange power constraints, node voltage constraints, and line power constraints.

4. The method according to claim 3, wherein The power balance constraint is: The microgrid output constraint is: The grid exchange power constraint is: The node voltage constraint is: The line power constraint is: Where i represents the microgrid index, i∈[1,N], t represents the scheduling period index, t∈[1,T], j represents the electric vehicle index, j∈[1,M], P MG,i,t represents the output of the i-th microgrid in period t, P V2G,j,t P represents the charging and discharging power of the jth electric vehicle in period t, grid,t represents the exchange power with the main network during period t, P load,i,t represents the total load demand during period t, Indicates the minimum and maximum output limits of the microgrid, Indicates the minimum and maximum power limits exchanged with the grid, V i,t represents the voltage of node i during period t, V i min 、V i max Indicates the minimum and maximum limits of the node voltage, P line,l,t represents the transmission power of line l in period t, Indicates the maximum transmission power limit of the line.

5. The method according to claim 1, wherein The charging and discharging and mobile energy storage-related constraints under V2G participation include V2G charging and discharging power constraints, electric vehicle SOC dynamic constraints, SOC operating range constraints, and charging and discharging mutual exclusion constraints.

6. The method according to claim 5, wherein The V2G charging and discharging power constraints are: The electric vehicle SOC dynamic constraint is: The SOC operating range constraints are: The charge and discharge mutual exclusion constraint is: Where t represents the scheduling period index, t∈[1,T], j represents the electric vehicle index, j∈[1,M], represents the charging power of electric vehicle j during period t, The discharge power of electric vehicle j in period t, SOC j,t represents the state of charge of electric vehicle j during period t, η ch ,η dis Respectively represent the charging and discharging efficiency, Δt represents the time interval, represents the battery capacity of electric vehicle j, Respectively represent the charging and discharging status indicator variables, It is a 0-1 variable.

7. The method according to claim 1, wherein Construct an energy transaction settlement model among microgrids, including: Determine the microgrid energy transaction costs and V2G service settlement costs, establish a transaction price mechanism, and determine the total energy transaction settlement costs. Determine the microgrid energy transaction costs, including: The transaction cost between microgrids is determined as: C tr,i,j,t =P tr,i,j,t ·l tr,t ·(1+β loss ) The transaction power balance constraint is determined as: The transaction power limit is determined as: C tr,i,j,t represents the transaction cost between microgrids i and j in period t, P tr,i,j,t represents the transaction power between microgrids i and j in period t, λ tr,t represents the transaction electricity price during period t, β loss represents the line loss compensation coefficient, i, j represent the microgrid index, i, j∈[1,N], t represents the scheduling period index, t∈[1,T], Indicates the maximum allowed transaction power; Determine the V2G service settlement costs, including: Determine the V2G service cost as: Determine the battery loss cost as: C deg,j,t =k deg ·|P V2G,j,t |·Δt The discharge power of electric vehicle j in period t, λ V2G,t represents the V2G service compensation price, P V2G,i,t represents the charging and discharging power of the jth electric vehicle in period t, k deg represents the battery loss coefficient, Δt represents the time interval; Establish a transaction price mechanism, including: The real-time transaction electricity price is expressed as: The V2G service compensation price is determined as: λ base is the basic electricity price, k dem represents the demand response coefficient, P dem,t represents the power demand during period t, Indicates the maximum required power, k V2G represents the V2G price adjustment coefficient, represents the required V2G response power, Indicates the maximum V2G responsiveness; The total cost of energy transaction settlement is expressed as:

8. A V2G-participated group microgrid energy trading and coordinated scheduling joint optimization system, characterized in that: include: a parameter acquisition module for acquiring system operating parameters, wherein the system includes a distributed power supply, an energy storage system, a group of electric vehicles, and charging station facilities within a group microgrid; The target construction module is used to construct the objective function of the joint optimization of group microgrid energy trading and V2G collaborative scheduling based on the obtained operating parameters; A constraint construction module is used to construct constraint conditions based on the acquired operating parameters, wherein the constraint conditions include basic constraints on system operation and constraints related to charging and discharging and mobile energy storage under the participation of V2G; Energy transaction model construction module, used to build an energy transaction settlement model between group microgrids based on the acquired operating parameters; The joint optimization module is used to perform joint optimization based on the constructed objective function, constraints and energy trading settlement model.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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